Novel Subject-Dependent Human-Posture Recognition Approach Using Tensor Regression

A novel dynamic human-posture recognition approach using tensor regression is proposed in this work. In our proposed approach, a new dynamic segmentation scheme based on hidden logistic regression (HLR) is first undertaken to segment multidimensional skeletal graph data. Within each segment of multidimensional data, a new feature tensor consists of high-dimensional skeletal-graph time-series (SGTS) involving multijoint 3-D coordinates and their temporal differences. Regression models can thus be trained from these collected feature tensors with respect to each type of human posture of interest. Experiments using real-world Kinect data are conducted to evaluate the effectiveness of our proposed novel tensor-based human-posture recognition scheme. In comparison with two prevalent deep learning models, namely the graph convolutional network (GCN) and the Transformer, our proposed novel tensor-based human-posture recognition approach can achieve the highest recognition accuracy of ${97}\%$ . Furthermore, we have evaluated the performance of our proposed new method using the open-source Kinect dataset, namely the UTKinect dataset, for one-shot learning. Our proposed novel tensor-based human-posture recognition approach still significantly outperforms the aforementioned prevalent deep learning models for one-shot learning.